As the processing applications of workpieces become more complex, the go/no go conditions of the workpieces are determined according to a variety of workpiece qualities. Take a processing machine of a bearing as an example. The workpiece qualities of the bearing manufactured by the processing machine include the height, the inlet diameter, the internal diameter and the ditch trail of the bearing. After the workpiece (i.e., bearing) is manufactured, it is necessary to measure the height, the inlet diameter, the internal diameter and the ditch trail of the bearing. According to the result of measuring these workpiece qualities, the go/no go conditions of the workpiece could be judged.
If one or more than one workpiece quality is unqualified, the workpiece is in the no go condition. For example, if the inlet diameter, the internal diameter or the ditch trail is unqualified, the workpiece (i.e., the bearing) is in the no go condition.
If the qualities of all workpieces manufactured by the processing machine are measured, the fabricating cost is very high. In views of cost reduction, the workpiece qualities are measured by sampling inspection. According to the result of the sampling inspection, the testing worker infers whether the unsampled workpieces are in the go condition or the no go condition. However, the sampling inspection approach cannot achieve the all-round quality control purpose.
Nowadays, a virtual metrology (VM) system is provided to predict the real-time workpiece qualities, monitor the performance of the processing machine and improve the production process. Since the virtual metrology system is able to predict whether the real-time workpiece qualities are abnormal, the problem of continuously manufacturing the no-go workpieces by the processing machine could be avoided. That is, the virtual metrology system could reduce huge loss.
For example, the virtual metrology system could allow the processing machine of the bearing to continuously operate while maintaining good yield. During operation of the processing machine, the virtual metrology system continuously predicts the workpiece qualities of the manufactured workpieces according to a real-time machine parameter set. If a workpiece quality (e.g., the inlet diameter of the bearing) is unqualified and the workpiece is in the no go condition according to the prediction result, the testing worker may adjust or replace the cutting tool. Consequently, the huge loss caused by continuously operating the processing machine could be avoided.
Moreover, the conventional virtual metrology system builds a prediction model of each workpiece quality according to the machine parameter set. Basically, the conventional virtual metrology system uses an algorithm such as Lasso Regression to acquire the prediction model. Hereinafter, the way of building the prediction model in the conventional virtual metrology system will be described by taking the processing machine of bearings as an example.
Generally, the processing machine of bearings is equipped with a machine monitoring module to monitor the statuses of all components of the processing machine in real time. That is, the machine monitoring module comprises plural sensors to sense the processing machine and the components and generate the machine parameter set.
For example, the machine monitoring module generates n machine parameters x1˜xn. These machine parameters x1˜xn are collected as a machine parameter set. The machine parameter set indicates the statuses of the processing machine and the components. The machine parameters of the machine parameter set include an environmental temperature parameter, a machine vibration parameter, a cutting force parameter, a cutting tool status parameter, a wear parameter, a cutting tool usage parameter, and so on.
The workpiece qualities of the bearing include the height, the inlet diameter, the internal diameter and the ditch trail of the bearing. That is, the four workpiece quality parameters of the bearing include a height profile y1, an inlet diameter profile y2, an internal diameter profile y3 and a ditch trail profile y4 of the bearing. These workpiece quality parameters y1˜y4 are collaboratively defined as a workpiece quality parameter set, indicating the workpiece qualities. In this context, the workpiece quality parameter set contains four workpiece quality parameters. It is noted that the number of the workpiece quality parameters in the workpiece quality parameter set is not restricted. For example, a thickness error of the bearing or any other appropriate workpiece quality parameter could be contained in the workpiece quality parameter set.
In the training stage of the virtual metrology system, a prediction model of a single workpiece quality is built by a specified algorithm according to the machine parameter set and a workpiece quality parameter. For example, the processing machine of bearings has to build four prediction models to predict the four workpiece quality parameters y1˜y4.
After the four prediction modules are built and during a predicting stage of the virtual metrology system, the four workpiece quality parameters y1˜y4 of the workpiece could be respectively predicted in real time according to the machine parameter set and the four prediction models. That is, the height profile y1 is predicted according to the machine parameter set x1˜xn and the first prediction model, the inlet error y2 is predicted according to the machine parameter set x1˜xn and the second prediction model, the internal diameter profile y3 is predicted according to the machine parameter set x1˜xn and the third prediction model, and the ditch trail profile y4 is predicted according to the machine parameter set x1˜xn and the fourth prediction model.
If the difference between one of the predicted workpiece qualities and the real workpiece quality is too large, it is necessary to modify the corresponding prediction model. For example, if the difference between the predicted inlet error y2 and the real inlet error is too large, it is necessary to modify the second prediction model.
As mentioned above, the conventional virtual metrology system builds the prediction model corresponding to the single workpiece quality. Each prediction model is used to predict one workpiece quality only. Moreover, the prediction models corresponding to different workpiece qualities are modified individually.